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"... SIFT descriptors are broadly used in various emerging applications. In recent years, these descriptors were deployed in compressed and binarized forms due to the computational complexity, storage, security and privacy cost incurred by working on real data. At the same time, the theoretical analysis ..."

SIFTdescriptors are broadly used in various emerging applications. In recent years, these descriptors were deployed in compressed and binarized forms due to the computational complexity, storage, security and privacy cost incurred by working on real data. At the same time, the theoretical analysis

"... In this paper we introduce a 3-dimensional (3D) SIFT descriptor for video or 3D imagery such as MRI data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D SIFT is able to outperform prev ..."

In this paper we introduce a 3-dimensional (3D) SIFTdescriptor for video or 3D imagery such as MRI data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D SIFT is able to outperform

"... Abstract. Maximally Stable Extremal Regions are robust to complex affine distortion and illumination changes between reference image and real-time image. On the basis of deeply research on the SIFT descriptor, this paper propose a description algorithm for MSER using SIFT descriptor. The Second cent ..."

Abstract. Maximally Stable Extremal Regions are robust to complex affine distortion and illumination changes between reference image and real-time image. On the basis of deeply research on the SIFTdescriptor, this paper propose a description algorithm for MSER using SIFTdescriptor. The Second

"... In this paper, a novel affine invariant descriptor for object matching is proposed. The advantage of Maximally Stable Extremal Regions (MSER) method is applied to get the most stable regions in the image. Inside each region, we pick the seeds as keypoints since MSER regions are invariant to affine t ..."

. Our experiments demonstrate that the proposed affine invariant local descriptor based on Voronoi tessellation is more stable and robust to object matching than SIFTdescriptor while using the same keypoints.

"... Abstract. In machine vision, Scale-invariant feature transform (SIFT) and its variants have been widely used in image classification task. How-ever, the high dimensionality nature of SIFT features, usually in the or-der of multiple thousands per image, would require careful consideration in place to ..."

Abstract. In machine vision, Scale-invariant feature transform (SIFT) and its variants have been widely used in image classification task. How-ever, the high dimensionality nature of SIFT features, usually in the or-der of multiple thousands per image, would require careful consideration in place

"... Several approaches to object recognition make extensive use of local image information extracted in interest points, known as local image descriptors. State-of-the-art methods perform a statistical analysis of the gradient information around the interest point, which often relies on the computation ..."

Several approaches to object recognition make extensive use of local image information extracted in interest points, known as local image descriptors. State-of-the-art methods perform a statistical analysis of the gradient information around the interest point, which often relies on the computation

"... Logos sometimes also known as trademark have high importance in today’s marketing world. Logo or trademark is of high importance because it carries the goodwill of the company and the product. Logo matching and recognition is important to discover either improper or unauthorized use of logos. Query ..."

images may come with different types of scale, rotation, affine distortion, illumination noise, highly occluded noise. Siftdescriptor, surf descriptor and hog descriptor are very good features to use among the existing techniques to recognize the logo images from such difficulties more accurately

"... The SIFT (Scale Invariant Feature Transform) is a computer vision algorithm that is used to detect and describe the local image features. The SIFT features are robust to changes in illumination, noise, and minor changes in viewpoint. The SIFT features have been used object recognition, image retriev ..."

retrieval and matching, and so on.. The research of SIFTdescriptors and improved SIFTdescriptors is important. In this paper, we conducted in-depth research on research results of SIFT in recent years, summed up the thinking and properties of SIFT algorithm. Focused on analyzing SIFT algorithm

"... Abstract. We propose reliable outdoor object detection on mobile phone imagery from o-the-shelf devices. With the goal to provide both robust object detection and reduction of computational complexity for situated interpretation of urban imagery, we propose to apply the 'Informative Descriptor ..."

Approach ' on SIFT features (i-SIFTdescriptors). We learn an attentive matching of i-SIFT keypoints, resulting in a signicant im-provement of state-of-the-art SIFTdescriptor based keypoint matching. In the o-line learning stage, rstly, standard SIFT responses are eval-uated using an information